Measuring the Uncertainty of Environmental Good Preferences with Bayesian Deep Learning
Measuring the Uncertainty of Environmental Good Preferences with Bayesian Deep Learning
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DOI：
10.1145/3524458.3547250
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Ricardo Flores;M. L. Tlachac;Elke A. Rundensteiner
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文献类型：
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作者：
Ricardo Flores;M. L. Tlachac;Elke A. Rundensteiner
由于气候变化和随之而来的自然灾害，人们对衡量社会产品对社会的价值越来越感兴趣，比如环境保护。传统上，陈述的偏好，如条件估值，通过支付意愿（WTP）范式捕捉了环境产品价值的经济学视角。其中，利用机器学习估计WTP的经济学理论是随机实用模型。然而，WTP的估计依赖于基于线性函数形式的相当简单的偏好假设。因此，这些模型无法捕捉人类决策过程中复杂的不确定性。此外，条件估值仅使用WTP的平均值或中位数估计。然而，人们已经认识到，WTP的其他分位数对于确保提供社会产品是有价值的。在这项工作中，我们建议利用贝叶斯深度学习（BDL）模型来捕获陈述偏好估计中的不确定性。我们关注的是为环境产品付费的概率和WTP的条件分布。贝叶斯深度学习模型通过个体偏好的随机成分与随机效用模型的经济学理论相联系。为了测试我们提出的模型，我们使用合成数据和真实世界的数据。综合数据的结果表明，BDL能较好地捕捉到不同WTP分布的不确定性。对于现实世界的数据，一项森林保护偶然价值调查，我们观察到WTP分布的高度变异性，表明个人对社会商品的偏好具有高度的不确定性。我们的研究可以用来为环境政策提供信息，包括保护自然资源和其他社会公益。
Due to climate change and resulting natural disasters, there has been a growing interest in measuring the value of social goods to our society, like environmental conservation. Traditionally, the stated preference, such as contingent valuation, captures an economics-perspective on the value of environmental goods through the willingness to pay (WTP) paradigm. Where the economics theory to estimate the WTP using machine learning is the random utility model. However, the estimation of WTP depends on rather simple preference assumptions based on a linear functional form. These models are therefore unable to capture the complex uncertainty in the human decision-making process. Further, contingent valuation only uses the mean or median estimation of WTP. Yet it has been recognized that other quantiles of the WTP would be valuable to ensure the provision of social goods. In this work, we propose to leverage the Bayesian Deep Learning (BDL) models to capture the uncertainty in stated preference estimation. We focus on the probability of paying for an environmental good and the conditional distribution of WTP. The Bayesian deep learning model connects with the economics theory of the random utility model through the stochastic component on the individual preferences. For testing our proposed model, we work with both synthetic and real world data. The results on synthetic data suggest the BDL can capture the uncertainty consistently with different distribution of WTP. For the real world data, a forest conservation contingent valuation survey, we observed a high variability in the distribution of the WTP, suggesting high uncertainty in the individual preferences for social goods. Our research can be used to inform environmental policy, including the preservation of natural resources and other social good.